Guides / How to Choose Chinese TTS Open-Source Models (2026-07) · 中文版
How to Choose Chinese TTS Open-Source Models (2026-07)
13 model cards for open-source Chinese TTS models, with key picks by use case, so you don't have to browse through each one.
Editorial picks
This page covers all 13 open-source TTS models on the site that can generate Chinese speech — 6 come with the key_info.tts field, and the other 7 (MOSS-TTS-v1.5, OmniVoice, etc.) are included through pipeline_tag because earlier card versions lacked that field. Below, picks highlight a few key ones by scenario; see the table for the rest. VRAM/language/license information is based on key_info, but when there's a conflict with community-measured results, the community measurements take precedence.
- Widest community validation / low-latency conversation Qwen3-TTS-12Hz-1.7B-CustomVoice (Qwen)
HF downloads 1.82 million, likes 1548, the largest community validation on this page; key_info.commercial_use indicates commercial use allowed, community.solves explicitly lists support for Chinese and 10 other mainstream languages, suitable for low-latency (97ms) voice interaction scenarios.
- Multilingual expressiveness / emotion control Higgs TTS 3 (Boson AI)
why_trust mentions the lowest WER across 4 benchmarks including SeedTTS/CV3, key_info.tts.languages clearly includes Chinese (100+ languages including Chinese, English, Japanese, Korean, Arabic, etc., with 85 languages having <5% WER); however, the license is boson-higgs-audio-v3-research-and-non-commercial-license, commercial_use is marked 'subject to verification', so direct commercial use is not allowed.
- Lightweight, CPU-friendly, commercial use allowed OmniVoice (k2-fsa)
key_info.size only 613M, license is apache-2.0 (commercial use allowed); community.solves mentions 'based on Qwen3-0.6B, can run on consumer hardware', watch_outs also confirms 'in environments with 8GB or less VRAM, TTS automatically offloads to CPU', making this the most hardware-friendly option on this page.
- Commercial-use long-text / multilingual cloning MOSS-TTS-v1.5 (OpenMOSS)
key_info.commercial_use indicates commercial use allowed (Apache-2.0), hf_raw.tags includes language tags like zh/yue; community.solves mentions 'provides a torch-free inference path based on llama.cpp/GGML, lowering the deployment barrier for Python environments', but watch_outs also cautions 'voice cloning quality is extremely sensitive to reference audio quality'.
Full comparison
| Model | Params | VRAM | License | Commercial use | Languages | China access | Deploy |
|---|---|---|---|---|---|---|---|
| Kokoro-82M (hexgrad) | 82M | ~0.1GB 4-bit | apache-2.0 | 可商用 | 8 种语言 | 需代理 | pip install kokoro && python -c "from kokoro import KPipeline; KPipeline('a'); print('Kokoro loaded')" |
| OmniVoice (k2-fsa) | 613M | ~0.4GB 4-bit | apache-2.0 | 可商用 | — | 需代理 | pip install omnivoice |
| Qwen3-TTS-12Hz-1.7B-CustomVoice (Qwen) | 1.7B | ~1.2GB 4-bit | Apache-2.0 | 可商用 | — | 魔搭可用 | pip install qwen-tts && python -c 'from qwen_tts import Qwen3TTSModel; model=Qwen3TTSModel.from_pretrained("Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", device_map="cuda:0")' / qwen-tts-demo Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice --ip 0.0.0.0 --port 8000 / modelscope download --model Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice --local_dir ./local_model |
| Higgs TTS 3 (Boson AI) | 4B | ~2.8GB 4-bit | boson-higgs-audio-v3-research-and-non-commercial-license | 需自查 | 100+ 语种 (含中/英/日/韩/阿拉伯等85种<5% WER) | 需代理 | sgl-omni serve --model-path bosonai/higgs-tts-3-4b --port 8000 / vllm-omni serve bosonai/higgs-tts-3-4b --host 0.0.0.0 --port 8095 --trust-remote-code --omni / Boson AI API (https://docs.boson.ai/models/higgs-audio-tts/overview) |
| MOSS-TTS-Local-Transformer-v1.5 (OpenMOSS) | 4.6B | ~2.7GB 4-bit | Apache-2.0 | 可商用 | 31种语言 (含中·粤·英·日·韩·法·德·西等) | 魔搭可用 | python -c "from transformers import AutoModel; model = AutoModel.from_pretrained('OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5', trust_remote_code=True)" / python -c "from modelscope import AutoModel; model = AutoModel.from_pretrained('OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5', trust_remote_code=True)" |
| MOSS-TTS-v1.5 (OpenMOSS) | 8.5B | ~5.1GB 4-bit | Apache-2.0 | 可商用 | — | 魔搭可用 | git clone https://github.com/OpenMOSS/MOSS-TTS.git && pip install -e . / transformers.AutoModel.from_pretrained('OpenMOSS-Team/MOSS-TTS-v1.5') |
| Gepard-1.0 (nineninesix) | 555M | ~0.3GB 4-bit | Apache 2.0 (codec: NVIDIA Open Model License) | 需自查 | 英语(美/英) · 西班牙语(墨) · 葡萄牙语(巴西) · 荷兰语 | 需代理 | vllm serve nineninesix/gepard-1.0 |
| Dramabox (ResembleAI) | — | — | other | — | — | 需代理 | huggingface-cli download ResembleAI/Dramabox / snapshot_download('ResembleAI/Dramabox') |
| Higgs-Audio-v3-TTS-4B (Boson AI) | 4B | ~2.8GB 4-bit | Boson Research & Non-Commercial License | 需自查 | 100+语言，含中、英、日、韩、法、德等83种高质量语种 | 需代理 | sgl-omni serve --model-path bosonai/higgs-audio-v3-tts-4b / Boson AI API (https://docs.boson.ai/models/higgs-audio-tts/overview) |
| scenema-audio (ScenemaAI) | — | — | other | — | — | 需代理 | huggingface-cli download ScenemaAI/scenema-audio / git clone https://huggingface.co/ScenemaAI/scenema-audio |
| ZONOS2 (Zyphra) | — | — | apache-2.0 | 可商用 | 英语(主)·中文(主)·日语(主) · 韩/俄/意/葡/法/西/越/德/希伯来/荷兰/瑞典等 32 语种 | 需代理 | git clone https://github.com/Zyphra/ZONOS2 && cd ZONOS2 && uv sync && uv run python -m minisgl --model-path Zyphra/ZONOS2 --tts-default-voices-dir ./default_voices/（需 NVIDIA GPU, Linux） |
| IndexTTS-2 | — | — | — | 需自查 | — | 魔搭可用 | 暂无已验证的一键部署命令，需参考官方GitHub仓库（https://github.com/index-tts/index-tts）手动安装 |
| MisoTTS-8B (MisoLabs) | 8B | ~4.9GB 4-bit | other | 需自查 | 未声明 | 需代理 | git clone https://github.com/MisoLabsAI/MisoTTS && cd MisoTTS && pip install -r requirements.txt / huggingface-cli download MisoLabs/MisoTTS --local-dir ./weights |
| Inflect-Nano-v1 (owensong) | 4.63M | ~0.1GB 4-bit | Apache-2.0 | 可商用 | 英文 | 需代理 | git clone https://huggingface.co/owensong/Inflect-Nano-v1 && cd Inflect-Nano-v1 && pip install -r requirements.txt && python inference.py --text 'Hello, world.' --out output.wav / git clone https://huggingface.co/owensong/Inflect-Nano-v1 && cd Inflect-Nano-v1 && pip install -r requirements.txt && python app.py |
Common pitfalls
- 仅限研究与非商业用途（Boson Higgs Audio v3 Research and Non-Commercial License） — bosonai/higgs-audio-v3-tts-4b
- 旧版仓库已废弃，文档分散在模型卡与 SGLang 使用指南中 — bosonai/higgs-audio-v3-tts-4b
- 全精度模型需 18-20GB 显存，8GB 显卡无法运行完整模式 — bosonai/higgs-tts-3-4b
- 8GB 显卡即使使用量化版本（QT8/QT4）也较慢，需借助 CPU 内存卸载 — bosonai/higgs-tts-3-4b
- 长文本批量生成时可能出现缺词，需用 STT 逐条校验 — bosonai/higgs-tts-3-4b
- 8GB 显卡无法使用声音克隆功能 — bosonai/higgs-tts-3-4b
- 训练数据主要来自 OpenAI 和 ElevenLabs 的合成输出，存在潜在版权与许可风险 — hexgrad/Kokoro-82M
- 对长叙事文本表现优于日常对话，对话场景下自然度可能不足 — hexgrad/Kokoro-82M
- 需要 espeak 作为系统依赖处理 OOD 回退及部分非英语语言，增加部署复杂度 — hexgrad/Kokoro-82M
- 最快的推理配置不一定是最佳音质，需在速度与质量之间权衡 — hexgrad/Kokoro-82M
- 现有托管 API 服务较少，不易找到现成的云端调用方案 — hexgrad/Kokoro-82M
- License未明确（unknown），商用需自行评估风险 — IndexTeam/IndexTTS-2
- 显存需求较高：推荐参数（如80-200）需根据显存调整，4GB可尝试但可能受限 — IndexTeam/IndexTTS-2
- 英文及跨语言效果论文有报但社区实测稀少，中文外场景效果不保证 — IndexTeam/IndexTTS-2
- 无原生流式支持，完整生成耗时较长，不适合实时播报 — IndexTeam/IndexTTS-2
- 宣传的精确时长控制功能实际未支持,与此前宣传不符 — IndexTeam/IndexTTS-2
- 仅支持英文和中文输出,覆盖语种有限 — IndexTeam/IndexTTS-2
- 目前仅限非商业用途免费,商业使用需单独授权 — IndexTeam/IndexTTS-2
- PyTorch 版本在 CPU 上推理较慢，尚无 ONNX/GGML 版本 — k2-fsa/OmniVoice
- Qwen3-TTS 的输出在一致性和部分场景下仍优于 OmniVoice — k2-fsa/OmniVoice
- Voice Design 功能稳定性不如 Voice Clone — k2-fsa/OmniVoice
- Voice Clone 虽稳定但参数调优难度更高 — k2-fsa/OmniVoice
- HuggingFace 部署未暴露 temperature 等控制参数，影响对比测试 — k2-fsa/OmniVoice
- 对非语言内容（如语气停顿）更敏感，容易产生不理想输出 — k2-fsa/OmniVoice
- 短文本也会出现幻觉/乱读，需通过减少大写等方式缓解但无法根除 — MisoLabs/MisoTTS
- 合成一致性不足，同一段话容易出现偏离 — MisoLabs/MisoTTS
- 作为 8B 参数模型，实际表现让人觉得训练不充分 — MisoLabs/MisoTTS
- 仅支持英语，不支持其他语言（包括德语） — nineninesix/gepard-1.0
- 需要 GPU 运行 — nineninesix/gepard-1.0
- 参数规模小（555M），独立使用时的通用能力（如工具调用）存疑 — nineninesix/gepard-1.0
- 与更大模型搭配使用的最佳方式尚不明确 — nineninesix/gepard-1.0
- 参考 Python 推理代码速度仅 RTF ~2（约 50% 实时），本地实时使用有压力 — OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5
- 服务化部署复杂，需处理参考音频编码、自回归生成、多码本采样与有状态解码器等多阶段流水线 — OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5
- 不指定语言标签时，部分语言质量可能相比 1.0 版本回退 — OpenMOSS-Team/MOSS-TTS-v1.5
- 生成质量对部署配置敏感，需调优才能达到最佳效果 — OpenMOSS-Team/MOSS-TTS-v1.5
- 音质存在可感知的缺陷,非生产级/非 SOTA — owensong/Inflect-Nano-v1
- 目前仅为实验性发布,作者表示若有足够关注才考虑加大训练预算做 v2 — owensong/Inflect-Nano-v1
- 推理速度偏慢，有用户反馈生成速度低 — Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice
- GPU 利用率仅约 12%，存在优化空间 — Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice
- 与 VibeVoice 相比表达力偏平淡/单调 — Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice
- VoiceDesign 模型设计的语音难以在下一次生成中保留复用 — Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice
- 社区反馈缺少阿拉伯语、越南语、荷兰语、罗马尼亚语、粤语等语言支持 — Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice
- 社区 faster 版本在 PyTorch ≤2.5.0 上 CUDA graph 捕获不可靠 — Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice
- 基础模型 LTX-2.3 音频分支仅训练在 ≤~20 秒片段上,长文本生成可能受限 — ResembleAI/Dramabox
- LTX-2 Community License 存在商业使用门槛(如 10M 下载量限制) — ResembleAI/Dramabox
- 克隆效果依赖参考音频质量,差音频会导致输出劣化 — ResembleAI/Dramabox
- 开发者自述 ID-LORA 方案质量很差 — ScenemaAI/scenema-audio
- 模型文件较大（全精度约 9.8GB，INT8 约 4.9GB） — ScenemaAI/scenema-audio
- 是扩散模型而非传统 TTS 管线，开发者提及存在常见问题但未具体列出 — ScenemaAI/scenema-audio
- 存在高频噪声，音质并非顶级，手机外放勉强可用 — Zyphra/ZONOS2
- 生成不稳定，偶尔出现 bloopers，beta 阶段可靠性不足 — Zyphra/ZONOS2
- The VRAM numbers in the table are estimates from a parameter quantization formula; actual TTS/speech model usage may be significantly higher — for example, k2-fsa/OmniVoice's structured field states Q4 ~0.4GB, but community measurements show real usage around 6.5GB and it automatically switches to CPU below 8GB, so please rely on community measurements or your own tests rather than just this column's numbers.
FAQ
- Can I run Chinese TTS with 4GB VRAM?
- Yes, but you need to pick the right models. owensong/Inflect-Nano-v1 (4.63M parameters), Aratako/Irodori-TTS-500M-v3 (key_info.vram about Q4 0.3GB) are very small; k2-fsa/OmniVoice's structured field says Q4 ~0.4GB, but community tests show real usage around 6.5GB and it automatically switches to CPU when below 8GB — in such conflicts, follow community measurements; 4GB VRAM is better suited to those definitively smaller models.
- How to choose a Chinese TTS model that allows commercial use?
- In the table, key_info.commercial_use marked 'commercial use allowed' includes k2-fsa/OmniVoice, Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice, OpenMOSS-Team/MOSS-TTS-v1.5, Zyphra/ZONOS2, Aratako/Irodori-TTS-500M-v3 (Japanese specialization); Boson AI's Higgs series and MOSS-TTS-Local-Transformer-v1.5 are marked 'subject to verification', so please check the license terms yourself before commercial use.
- Can I run it on CPU only, without any GPU?
- Supertone/supertonic-3 is an ONNX model; community.solves confirms it can run offline without GPU (99M parameters); k2-fsa/OmniVoice will also automatically fall back to CPU when VRAM is below 8GB, but speed will decrease.